Carbon-13 solid-state NMR of soil organic matter - using the technique effectively
Bibliographic record
Abstract
Solid-state 13C CPMAS NMR offers many options for characterizing carbon in soil organic matter (SOM). Its effectiveness, however, is often limited by a poor understanding of the techniques, and lack of hands-on access and training opportunities for students. Of nearly 250 modern NMR systems in Canada, approximately one is genuinely available for SOM studies, and there is poor communication between NMR operators and SOM users. While quantitative reliability can be addressed to some extent by multiple contact-time experiments or single-pulse (Bloch decay) spectra, it is also important to consider the effects of spectrometer background, spinning sidebands (especially with higher magnetic fields) and processing operations such as line-broadening, phasing and baseline correction. In many studies, more consideration needs to be given to instrument specifications, the type of information needed, and whether sample fractionation or pretreatment should be used. Structural information can be greatly enhanced by dipolar-dephasing and sideband suppression sequences. Sequences based on relaxation differences can reveal pools of carbon with different structures. Data analysis can be enhanced by principal component analysis, spectrum deconvolution and difference spectra. Studies of xenobiotics and C metabolism can be greatly aided by 13C-labeling. However, a key limitation to SOM applications remains the gap in culture and expectations of the users. Key words: Soil organic matter, 13C CPMAS NMR, Bloch decay, dipolar dephasing, spinning sidebands
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".